Using Empirical Recurrence Rates Ratio for Time Series Data Similarity

IEEE Access

Justin Zhan¹  Moinak Bhaduri² 

[1] Department of Computer Science, University of Nevada at Las Vegas, Las Vegas, NV, USA;Department of Mathematical Sciences, University of Nevada at Las Vegas, Las Vegas, NV, USA;

关键词: Time series;    classification;    database clustering;    similarity measures;    empirical recurrence rates;    empirical recurrence rates ratios;   

DOI  :  10.1109/ACCESS.2018.2837660

来源: DOAJ

【 摘 要 】

Several methods exist in classification literature to quantify the similarity between two time series data sets. Applications of these methods range from the traditional Euclidean-type metric to the more advanced Dynamic Time Warping metric. Most of these adequately address structural similarity but fail in meeting goals outside it. For example, a tool that could be excellent to identify the seasonal similarity between two time series vectors might prove inadequate in the presence of outliers. In this paper, we have proposed a unifying measure for binary classification that performed well while embracing several aspects of dissimilarity. This statistic is gaining prominence in various fields, such as geology and finance, and is crucial in time series database formation and clustering studies.

【 授权许可】

Unknown